Compass用eROI框架区分了两个看似相当的AI项目，一个赚九位数，另一个被砍掉。想避开AI投资坑的可以看看这个拆解方法。
房地产经纪公司Compass的两个AI项目结果截然不同：Likely-to-Sell推荐项目带来了九位数的年佣金收入，而Time-on-Market定价工具被合理搁置。单纯的ROI估算无法区分二者。新提出的eROI框架将项目分解为三个独立组件：成功价值、成功可能性和所需投资。每个组件对应高管在开发前可以回答的问题，从而打破先有项目才能估算ROI的循环。该框架还要求先确保有足够的好点子，再对项目排序，并组合投资组合而非仅资助排名第一的项目。
AI Strategy: How to Choose What AI Product to Implement
Firms struggle to choose AI projects that pay off: two projects can look equally promising to smart, motivated stakeholders and yet deserve opposite decisions. At the residential real-estate brokerage Compass, one AI product (Likely-to-Sell recommendations) flagged sales outreach opportunities and went on to account for nine figures in annual gross commission revenue. Another championed AI product (a Time-on-Market pricing tool) was rightly shelved. A simple ROI estimate could not distinguish the two. We present expected ROI (eROI), a framework that decomposes each bet into three components and rates them separately: Value if Successful, Likelihood of Success, and Investment Required. Each maps to a question executives can answer before building: How valuable would it be if it worked? How likely is it to work? And what would it cost to implement? Separating the three breaks a common catch-22: teams cannot estimate ROI until they know whether a project will work, yet cannot know whether it will work without building it. Judging Value if Successful on its own dissolves the loop, letting a team argue that a product would be valuable if it worked while it weighs how likely that is. The framework also asks, before ranking anything, whether there are enough good ideas on the table. After ranking, it guides assembling a portfolio of bets rather than funding only the single top-ranked project. We illustrate eROI on Compass's candidate AI products. Precise ROI estimates are hard to make given the inherent uncertainty of AI projects. Coarse business-level ratings of the three components are enough to tell strong bets from weak ones.